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authorNirjas Jakilim <Nirzak@users.noreply.github.com>2021-04-21 13:41:13 +0600
committerGitHub <noreply@github.com>2021-04-21 13:41:13 +0600
commitc559bc8038ab684c68601757d2735c7be39befc4 (patch)
treed5be862cca739810d395993183f0e6a480978d41 /doc
parentdabc5368c7a2dec101b1b52690e39e91c8f67bce (diff)
downloadnumpy-c559bc8038ab684c68601757d2735c7be39befc4.tar.gz
Minor fix to add reference link to numpy.fill_diagonal function
Diffstat (limited to 'doc')
-rw-r--r--doc/source/user/tutorial-svd.rst2
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/source/user/tutorial-svd.rst b/doc/source/user/tutorial-svd.rst
index fd9e366e0..7b905e51e 100644
--- a/doc/source/user/tutorial-svd.rst
+++ b/doc/source/user/tutorial-svd.rst
@@ -431,7 +431,7 @@ Now, to build our approximation, we first need to make sure that our singular
values are ready for multiplication, so we build our ``Sigma`` matrix similarly
to what we did before. The ``Sigma`` array must have dimensions
``(3, 768, 1024)``. In order to add the singular values to the diagonal of
-``Sigma``, we will use the ``fill_diagonal`` function from NumPy, using each of
+``Sigma``, we will use the `numpy.fill_diagonal` function from NumPy, using each of
the 3 rows in ``s`` as the diagonal for each of the 3 matrices in ``Sigma``:
::